{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 真实世界的数据集\n",
    "# 对于每一个数据集，用各种方法，从原始数据中提取有意义的内容\n",
    "\n",
    "# the case 3\n",
    "# the dataset babyname comes form the SSA\n",
    "\n",
    "# the overview of this dataset\n",
    "\n",
    "# 美国社会保障总署（SSA） 提供了一份从 1880 年到现在的婴儿名字频率数据。\n",
    "\n",
    "# Hadley Wickham（许多流行 R 包的作者）经常用这份数据来演示 R 的数据处理功能。\n",
    "\n",
    "# 要做一些数据规整才能加载这个数据集， 这么做就会产生一个如下的 DataFrame："
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "names.head(10)\n",
    "\n",
    "name sex births year\n",
    "0 Mary F 7065 1880\n",
    "1 Anna F 2604 1880\n",
    "2 Emma F 2003 1880\n",
    "3 Elizabeth F 1939 1880\n",
    "4 Minnie F 1746 1880\n",
    "5 Margaret F 1578 1880\n",
    "6 Ida F 1472 1880\n",
    "7 Alice F 1414 1880\n",
    "8 Bertha F 1320 1880\n",
    "9 Sarah F 1288 1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可以用这个数据集做很多事， 例如：\n",
    "#     1、计算指定名字（可以是你自己的， 也可以是别人的） 的年度比例。\n",
    "#     2、计算某个名字的相对排名。\n",
    "#     3、计算各年度最流行的名字， 以及增长或减少最快的名字。\n",
    "#     4、分析名字趋势： 元音、 辅音、 长度、 总体多样性、 拼写变化、 首尾字母等。\n",
    "#     5、分析外源性趋势： 圣经中的名字、 名人、 人口结构变化等。\n",
    "\n",
    "# 利用前面介绍过的那些工具， 这些分析工作都能很轻松地完成\n",
    "\n",
    "# 到时为止， 美国社会保障总署将该数据库按年度制成了多个数据文件， 其中给出了每个性别/名字组合的出生总数。\n",
    "#   这些文件的原始档案可以在这里获取： http://www.ssa.gov/oact/babynames/limits.html。\n",
    "# 如果在阅读的时候这个页面已经不见了， 也可以用搜索引擎找找。\n",
    "\n",
    "# 下载 \"National data\" 文件 names.zip， 解压后的目录中含有一组文件（如yob1880.txt）\n",
    "#    用 UNIX 的 head 命令查看了其中一个文件的前 10 行\n",
    "#    在 Windows 上，可以用 more 命令，或直接在文本编辑器中打开"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "!head -n 10 datasets/babynames/yob1880.txt\n",
    "Mary,F,7065\n",
    "423Anna,F,2604\n",
    "Emma,F,2003\n",
    "Elizabeth,F,1939\n",
    "Minnie,F,1746\n",
    "Margaret,F,1578\n",
    "Ida,F,1472\n",
    "Alice,F,1414\n",
    "Bertha,F,1320\n",
    "Sarah,F,1288"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Woodie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Worthy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>York</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Zachariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births\n",
       "0          Mary   F    7065\n",
       "1          Anna   F    2604\n",
       "2          Emma   F    2003\n",
       "3     Elizabeth   F    1939\n",
       "4        Minnie   F    1746\n",
       "...         ...  ..     ...\n",
       "1995     Woodie   M       5\n",
       "1996     Worthy   M       5\n",
       "1997     Wright   M       5\n",
       "1998       York   M       5\n",
       "1999  Zachariah   M       5\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于这是一个非常标准的以逗号隔开的格式， 所以可以用 pandas.read_csv 将其加载到 DataFrame 中\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "dataset_path = './../dataset/'\n",
    "\n",
    "names1880 = pd.read_csv(dataset_path + 'babynames/yob1880.txt', names=['name', 'sex', 'births'])\n",
    "\n",
    "names1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这些文件中仅包含当年出现超过 5 次的名字\n",
    "# 为了简单处理，可以用 births 字段列 的 sex 分组小计 表示该年度的 births 总计\n",
    "\n",
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name sex  births  year\n",
       "0        Mary   F    7065  1880\n",
       "1        Anna   F    2604  1880\n",
       "2        Emma   F    2003  1880\n",
       "3   Elizabeth   F    1939  1880\n",
       "4      Minnie   F    1746  1880\n",
       "5    Margaret   F    1578  1880\n",
       "6         Ida   F    1472  1880\n",
       "7       Alice   F    1414  1880\n",
       "8      Bertha   F    1320  1880\n",
       "9       Sarah   F    1288  1880\n",
       "10      Annie   F    1258  1880\n",
       "11      Clara   F    1226  1880\n",
       "12       Ella   F    1156  1880\n",
       "13   Florence   F    1063  1880\n",
       "14       Cora   F    1045  1880\n",
       "15     Martha   F    1040  1880\n",
       "16      Laura   F    1012  1880\n",
       "17     Nellie   F     995  1880\n",
       "18      Grace   F     982  1880\n",
       "19     Carrie   F     949  1880"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 该数据集按照年度被分隔成多个文件\n",
    "# 首先需要将所有数据都组装到一个 DataFrame 里面，加上 year 字段\n",
    "# 使用 pandas 的 pandas.concat 即可完成这个目的\n",
    "years = range(1880, 2011)\n",
    "\n",
    "pieces = []\n",
    "columns = ['name', 'sex', 'births']\n",
    "\n",
    "for year in years:\n",
    "    path = dataset_path + 'babynames/yob%d.txt' % year\n",
    "    frame = pd.read_csv(path, names=columns)\n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "    \n",
    "# concatenate everything into a single DataFrame\n",
    "names = pd.concat(pieces, ignore_index=True)\n",
    "\n",
    "# 需要注意几点：\n",
    "#    1、concat 默认是按照行将多个 DataFrame 组合到一起\n",
    "#    2、必须指定 ignore_index=True ，因为不需要保留 read_csv 所返回的原始行号\n",
    "\n",
    "# 现在得到了一个非常大的 DataFrame ，包含所有的名字数据\n",
    "names[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1690784, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2006  1896468  2050234\n",
       "2007  1916888  2069242\n",
       "2008  1883645  2032310\n",
       "2009  1827643  1973359\n",
       "2010  1759010  1898382"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 有了这些数据之后，\n",
    "# 可以利用 groupby 或者 pivot_table 在 year 和 sex 级别上对其进行聚合\n",
    "\n",
    "total_births = names.pivot_table('births', index='year', columns='sex', aggfunc=sum)\n",
    "\n",
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b40e08>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 利用 pandas 的 DataFrame 绘制线图\n",
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 接下来插入一个 prop 列，用于存放指定名字的婴儿数相对于总出生数的比例\n",
    "# prop 值为 0.02 表示每 100 名婴儿中有 2 名婴儿取了当前这个名字\n",
    "\n",
    "# 先按 year 和 sex 分组\n",
    "# 然后再将这个新的列 加到各个分组上\n",
    "\n",
    "def add_prop(group):\n",
    "    group['prop'] = group.births / group.births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)\n",
    "\n",
    "# 现在完整的数据集就有了以下这些列\n",
    "names[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1690784, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "            ... \n",
       "2008  M      1.0\n",
       "2009  F      1.0\n",
       "      M      1.0\n",
       "2010  F      1.0\n",
       "      M      1.0\n",
       "Name: prop, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在执行这样的分组处理时，一般都需要做一些有效性检查\n",
    "# 比如验证所有分组的 prop 的总和是否为 1\n",
    "names.groupby(['year', 'sex']).prop.sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name sex  births  year      prop\n",
       "0            Mary   F    7065  1880  0.077643\n",
       "1            Anna   F    2604  1880  0.028618\n",
       "2            Emma   F    2003  1880  0.022013\n",
       "3       Elizabeth   F    1939  1880  0.021309\n",
       "4          Minnie   F    1746  1880  0.019188\n",
       "...           ...  ..     ...   ...       ...\n",
       "261872     Camilo   M     194  2010  0.000102\n",
       "261873     Destin   M     194  2010  0.000102\n",
       "261874     Jaquan   M     194  2010  0.000102\n",
       "261875     Jaydan   M     194  2010  0.000102\n",
       "261876     Maxton   M     193  2010  0.000102\n",
       "\n",
       "[261877 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 工作完成\n",
    "# 为了便于实现进一步分析，需要取出该数据的一个子集：\n",
    "#    每对 sex/year 组合的前 1000 个名字\n",
    "# 这又是一个分组操作\n",
    "\n",
    "def get_top1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n",
    "\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "\n",
    "top1000 = grouped.apply(get_top1000)\n",
    "\n",
    "# drop the group index, not needed\n",
    "top1000.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# 现在的结果数据集就要少了许多\n",
    "top1000"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# 以上逻辑实现 也可以 DIY\n",
    "pieces = []\n",
    "for year, group in names.groupby(['year', 'sex']):\n",
    "    pieces.append(group.sort_values(by='births', ascending=False)[:1000])\n",
    "    \n",
    "top1000 = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131 entries, 1880 to 2010\n",
      "Columns: 6868 entries, Aaden to Zuri\n",
      "dtypes: float64(6868)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "# 接下来就可以针对这个 top1000 数据集进行分析工作\n",
    "\n",
    "# 分析命名趋势\n",
    "\n",
    "# 有了完整的数据集和刚才生成的 top1000 数据集\n",
    "# 就可以开始分析各种命名趋势，首先将前 1000 个名字分为两个部分\n",
    "\n",
    "boys = top1000[top1000.sex == 'M']\n",
    "\n",
    "girls = top1000[top1000.sex == 'F']\n",
    "\n",
    "# 这是两个简单的时间序列，\n",
    "# 只需要稍作调理即可绘制出相应的图表（比如每年叫做 John 和 Mary 的婴儿数）\n",
    "\n",
    "# 先生成一张按 year 和 name 统计的总出生数 透视表\n",
    "total_births = top1000.pivot_table('births', index='year', columns='name', aggfunc=sum)\n",
    "\n",
    "# 现在用 DataFrame 的 plot 方法绘制几个名字的曲线图\n",
    "total_births.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000000018296E48>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000001B9157C8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000001B941E08>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000001B97BDC8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]\n",
    "\n",
    "subset.plot(subplots=True, figsize=(12, 10), grid=False, title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从图中可知，这几个名字在美国人心目中已经不再风光了\n",
    "# 当然事实并非如此简单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba4eb88>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a69hf688qhLRMly5zEEz9Akz/O8gf3mfbzRjTv/QooMfOWASUceiMRQL8AnclTB3wVVXtNFL3dUCPZ299M5t21xJtURqbo5RVN7C9qoGaxgiNzS3sqm5g9Y59bNpdiyqEQ0I05gCQnR5m+ugCMtNDtKhS0xhlb10TaeEQpx85lM9MHMKu6kbW7azmmOI0rtj5H4Q2LYAvPkjF8FPJz0onIy3mQqNIE5S+B5vfgdpdUF/lzgYk5A4O2z8ABDLyICMHCsfB2Nkw9hRXq8/M7/NtmBIiTa6vpW6P/1vhms72bYOmWmiJuoOqRl1zWUvUL7ccWI42Q6TR/Y02QkvEpdWWA39DIQiltXmE23me7prs0nNcRSE9B9KzD7yWnu36hdKz/fPY11vfl2l9QCmoxzX03tAfAnqioi1KSEBEqG5opmxfA+vKanhv0x5WbNtLtEUJh4TczDQKs9PZW9/Moo0VNEZaABf465ujHDEsj4umj+TPH+9ixba9hARGF2UzsiCb4rxMhuRlMDQvk6Exy+lhoaG5hXAIjk7bSe6G+S7gNFbD7k9ckI82uSAw5jMw6jh3VjBxDhSNT+ZmS64Wf+ZUWw61u/3fcheo92yAys1QW+ECeFNN/DxC6a6PQ0IgYfc3FHZBUsJ+2a9Ly3BnWeFMCKe7/SGhmIf4A0Ak5hFt/3m06UBTYlOtO2h0mbgAnzMEhk52/TjFR8HQo2DkDFcpMAOOBfQkaGiOsnLbXkYWZjOqIIvXV5dx7/w1bKmo49iSAs6dOoLGSAubd9eyc18Du2saqahpYm99++3zIjB+SC5FOelkpYcZnJvBxKIwM0MbOLZxKYU7/oaUr4VIvau9zb0PTrg2dWppLVFo2OuuSqreCZWboGqrC8jN9VBT5pqzqne4IB4vCEoYisa5g13uMMgZ7PpLcor8X/88f6QLhKF+cjN1tNl9x+Z61ywXaXB/mxvivFZ/cNqaMihf6yoAkXqXXzgDxsyGiWfCxM+6ikAonNzvaBJiAb2faIq0UFXfxLD8rA7TVNS64F5e00g0qmRnhGlojrJq+z5Wb99HdWMzDc0tlFc3sq2qfn8zUEF2OrnpwvDIdn4QfoTjmpdRO+5ssqddRGjUDBgx3dUek03VBeH9TRy+mWPvVheQ63ytubHG/612te2GvfHza22WyBvm+igGjXLBOneou2pp/99iF6zDQVzcNQC1tLgrunZ9DFv+BhsXuH4dcJ3wE86ESZ91HfNDjkidikCKsYCewiLRFjbtrmXplko+Kt1LtKWFtHCINduqOGnH7/iHtBcokDoAGiWLHQXHExk1i2ETpjFo5BEuwMc2DWiLq/WhLjDmjXCvtbYt11VAwz7XpBBOdx25teUu2EYaXfNAzS5XS440HGhjbmlx/QH1lS5gt3OlUEvWYDRnMKGsfCQjDzLz0Yw8mjMKaEovoCFtEPXhQVSnFVGVOYrKjOFkZGSRkxEm2qLUNUWpb464v01R6vyjvilCbVOUpkiLazHBNaEJgIAg+18PiV/2K0UgLSRkp4fJzgiTkxEmOyONnHS3nBYOUdcUob4pSn5WOkPzMhiSl0lxXiaDstOQ/hwYa3fDprdgw5vusa/UvZ5V6Grtgye64D7uFBgxo/+csRzGLKAfprZV1fPOunJ2la5Dtn/IiKqlTG9azhGynZAktt9bJI2QRhL+zJa0LKI5w4jkDCMazqZFQrQQpoUQEQlTI3lUaT4VLbmUR3PZ3phNaWM2m+uzWFc/iDrc2UtmWoj8rLT9Abkn0sNCTkYauRnh/Z3QCrSouv5KvylUFcX3e8YsgxLxB4sm3y/Slc8ekpvJ0PyD+0eK2/SVDM3LZHBuRnLveFZ1zTJbF0Hp+7BzpWvWqq9067MHu7b3ovFQMBoyC1xH/EGPQa5tPpTumnCiTe7ADq5/AVwloKnGNfukZcOgke7+DpMQC+hmv/qmKGu2lrFp7XIqd2ykoamZxqYITc0RGiMR6ptaqGoO0RRVRkkFo2Q3jZrBHvKp1Hz2kE+1ZpNOlAwiVJNNuRawj1waSSf+fWaHys0IU5zvAllxfub+5SF5GdQ3RSnb10BNY5S8zDA5GWnkZaaRkxl2fzPSyM0Mk5uRRnZGmMbmFuqaIoRD4mvQab4WHSY7PUx6OLhapTsLiOyv/dc3R2mOtpDjy1Ld0Mzu6iZ21zT6R5PvH4ldbqIpeuiBQQQG57gAP2xQJlNHFTCjpIBjxxQyqiAreTX96p2w8S3XRFP+8cFBPhDizgRGTIPh0/3faa75rD+f3SSJBXTTZc3RFmobI1Q3RKhpPPCob23KaI7S0BQlKyNMYXY66eEQjRFXg00LC+nhkH8cWM5ICzHU11ZzMg7TdmzcmcC+hsj+4N4a8MtjlrdV1bN2ZzXNUff/OTQvg2NGDqKkKIcxg7MZOziHMUU5HDUin6z0JHRmNjf4/o19ro8j9tFUe+BqnXCG698Af4Oe+lp8nmt2a6pzVxyVrThwRtAqqxBGz4QJZ8ARn3OB3lhAN2YgamiO8vHOapaXVvHR1r2s31XN1sp69tQ27U+THhamjy7g6JGDGJSVzuDcdI4bU8SMMQVkpg3Aq1Yaq6FstQ/wK+DTRe6sAGDcaXDKt9zd1YfxPRcW0I1JITWNEUor69i8u45lW6t4f/MeNu+uZV9D8/4afUZaiJMnDuHzU4dzzpThHV5Z1e9Vl8GK38OiBw502uYMgRHHuqtyJn0Whk89bJpnLKAbcxhQVSrrmlmyeQ+LNu7hjY/L2FJRRzgknHXUMC4/cQxnHVVMWoB9Cn0q2gzr/+xq7Hs2wdb3oHyNW5c3HCaeBUedB0ec4wbWS1EW0I05DKkq68pqeH7ZNp5dWkp5dSPF+ZlcekIJX549ljGDU+BO0X3b/SWXf3GP+j3uappJn4VjLoTJ57n7EFJIEINznQf8HAgDD6vqfW3WjwMeAYqBPcDVqlraUZ4W0I3pO83RFhasLefp9z/lzbXlqCpzp4/kG2dMYnpJiozs2RJ1be5r/ugerc0zucUw5Eh3V+wxF8OwYwZ080xPB+cKA+uAc3CTV7wPXKmqq2PS/B43GuNjIvJZ3ABd13SUrwV0Y5Jjx956Hnt3C79bvIXqhggXzRjFd889KjVq7K1UYccyd7llxXrYtQa2LQXUzVUw5WIX3EcdP+CCe08D+snA3ap6rn9+J4Cq/jAmzSrgXFUt9aMv7lXVDmdusIBuTHJVNzTz329v5KG/biTaolx47CiuPWU8x40pTHbRekd1GXz8J1d73/S2u4N58CQ49nLXPDNsyoAI7j0N6POA81T1a/75NcBsVb0pJs0TwGJV/bmIfAn4H2Coqla0ySt2xqKZW7Zs6cHXMsYEYefeBh58awPPLi2lpjHC7AmD+c45k5k9cUiyi9Z76va4wL7i97D5r+61/FGu5n7SjTBkUnLL14GeBvTLcLXv2IB+kqp+KybNKNyY6BOAt4FLgamq2s5oSlZDN6a/qW5o5pklpTz41gbKqxuZPWEwl80aw3nTRpCXmcI3gu3b4a6e+eQ1WPuyuyFq8rkwbZ7728+miez1Jpc26fOAj1W1pKN8LaAb0z/VN0X5zaLN/GbRFrbuqScnI8zlJ47h66dPZFRhdrKL17uqd8L7D8MHj7thh8MZUHISjP0MjD0ZxpyY9OkhexrQ03CdomcD23Cdolep6qqYNEOBParaIiL3AlFV/X5H+VpAN6Z/U1WWbqnkifc+5YVl2wkJHD+2iCOG5TFtVAHnTh3OkLzMZBezd7S0uNnD1vwRtrwLOz7y4+uLG6DslG/B1C8lZfTJIC5bPB/4Ge6yxUdU9V4R+Rdgiaq+6NvZf4gbxO5t4Juq2thRnhbQjRk4SivrePSdzSzbWsX68hqq6poJh4RTJg1hRkkhRw7PY/LwfCYW5+4fciASbaG2MUp1Y/P+MYG2VNTx7vrdLNtaRX5WGiMKspg+uoDzpo3giGH9+Hb+plooXeIui1z1nLuhafg0OOErMOls1+beRx2qdmORMSYwqsrasmpeXLad11eXsdHPywtu3t2inAxqGyPUN8cf9nhwbgazxhXREGlhe1U963e5KQAnFudy1lHDOHNyMSdNGJycQccS0RJ1Qf3tHx8YZ6b4aDj1Vpg+r9cnkbGAbozpNY2RKJt217KurIZPyqopr24kLzONvKw08rPSyffLeZlpDB+UxZHD8gjFjPu+c28Dr63eyeury1i8aQ9NkRay0t1YNBfNGMVFM0YFOgRyoPZsgg1vwJJfQ9lKKBjjpn087io3ZnwvsIBujBkQ6puiLNpYwYK1u/jL2l1s3VPP6MJsvnb6BC4/cUz/HXZZFda9Cov+y13jLiE35O8JX3HDDwRYa7eAbowZcFSVN9fu4oEFG3h/cyVFOelcd8oEvnLyOIpyM5JdvPbt2Qgf/hY+/B3U7HRDDxx3FRz3ZRg6ucdt7RbQjTED2pLNe3jwrQ38ec0ucjLCXHHiWG44fQKj+/NllNGIu779g8dh3SvuKpmMPBfUZ14HM6/tVrYW0I0xKWHtzmp++dYGXvhoO6rK56eM4CunjOPkiUP692Tc1Tth7XzY9THsXgtTLoFZ13crKwvoxpiUsq2qnt8u2sJT731KZV0zk4fncc3J4zl/2ojUvTbes4BujElJDc1R/vjRdh5buJmV2/YhAtNHF3DypCEcP6aI6SUFjBiURTjUj2vvXWQB3RiT0lSVVdv38ebHu3hrXTkflVbtn44vLSSMKMji9COHcslxozlp/OCDLpscaCygG2MOKw3NUVbv2Mfq7fvYsbeeTbtrWbC2nLqmKOOG5HD9qROYN7OE3AE46FhfzFg0FngMKPRp7lDV+R3laQHdGNOX6poivLaqjMcWbubDT6soyE7nqtljue6U8QwfNHAm0e6LGYseAj5U1QdEZAowX1XHd5SvBXRjTLIs3VLJw3/dyKurdhIOCRcdO4obTp/A1FH9fzq+jgJ6IucbJwHrVXWjz+wp4BJgdUwaBVoHDS4Atne/uMYY07tmjiti5riZfFpRxyPvbOKZJVv5w4fbOPvoYdxzyVRKigbmdHyJDJAwGtga87zUvxbrbuBqESkF5gPfIg4RuVFElojIkvLy8m4U1xhjgjN2SA53XzyVhXeeze3nHsXCjRV8/qdv8+g7m4hEW5JdvC5LJKDH6w5u205zJfCon9TifOA3InJI3qr6kKrOUtVZxcXFXS+tMcb0goLsdL551hG8eusZzBxXxN1/XM0F9/+Nv34ysCqeiTS5lAJjYp6XcGiTyg3AeQCqulBEsoChwK4gCmmMMX1hzOAcHr/+JF5ZuZP/+/IarvnVe0zyw/rOnT6CE8YW9es7UhMJ6O8DR4rIBNyMRVcAV7VJ8yluRqNHReQYIAsYWIc2Y4wBRIS500dy1tHD+P2Srby2uozHF27h4b9tYuqoQXz11Al88fjR/fJmpaBmLJoC/DeQh2uO+a6qvtZRnnaVizFmoKhtjPDCsu08+u4m1pXVcPSIfO6+eCqfmTikz8tiNxYZY0wAVJWXV+7k3pfWsK2qntkTBjNvZgnnTx/ZZzcpWUA3xpgA1TdFefTdzTz9/qdsrqhjWH4mP738OE49Ymivf3ZHAb2fzutkjDH9V3ZGmP81ZxJv3jaHJ7/+GfKz0rj6V4v54fw11DfFn0u1L1hAN8aYbhIRTp40hD9963SuPGksv3x7I2f9ZAHPLi3dP3F2n5bHmlyMMSYY723aw70vreaj0r2MLszmslklXHpCCWMGB3fnqbWhG2NMH2lpUV5dtZMn3vuUv63fjSpMLM7llElDGJyTQVZGmJlji5jdzStkejqWizHGmASFQu469rnTR7J1Tx2vry7jrXXlPP/hdmqbIqjCP8yZ1O2A3hEL6MYY00vGDM7h+tMmcP1pEwB32WNjpPfGiLGAbowxfUREyEoP91r+dpWLMcakiIQCuoicJyJrRWS9iNwRZ/1PRWSZf6wTkargi2qMMaYjnTa5+BmL/ouYGYtE5MXYGYtU9dsx6b8FHN8LZTXGGNOBRGro+2csUtUmoHXGovZcCTwZROGMMcYkLqgZiwAQkXHABOAv7ay3GYuMMaaXBDVjUasrgGdVNe5gBjZjkTHG9J5EAjLH7owAABPlSURBVHoiMxa1ugJrbjHGmKRIJKDvn7FIRDJwQfvFtolE5CigCFgYbBGNMcYkotOArqoR4CbgVWAN8IyqrhKRfxGRi2OSXgk8pckaHMYYYw5zCd0pqqrzgfltXvt+m+d3B1csY4wxXWV3ihpjTIqwgG6MMSnCAroxxqQIC+jGGJMiLKAbY0yKsIBujDEpwgK6McakCAvoxhiTIiygG2NMighkxiKf5u9EZLWIrBKRJ4ItpjHGmM4EMmORiBwJ3AmcqqqVIjKstwpsjDEmvqBmLPo68F+qWgmgqruCLaYxxpjOBDVj0WRgsoi8IyKLROS8eBnZjEXGGNN7gpqxKA04EpiDG0b3YREpPORNNmORMcb0mqBmLCoFXlDVZlXdBKzFBXhjjDF9JKgZi54HzgIQkaG4JpiNQRbUGGNMx4KasehVoEJEVgNvArerakVvFdoYY8yhJFkzxs2aNUuXLFmSlM82xpiBSkSWquqseOvsTlFjjEkRFtCNMSZFWEA3xpgUYQHdGGNShAV0Y4xJERbQjTEmRVhAN8aYFGEB3RhjUoQFdGOMSRGBzFgkIteJSLmILPOPrwVfVGOMMR0JZMYi72lVvakXymiMMSYBQc1YZIwxJsmCmrEI4FIRWS4iz4rImDjrbcYiY4zpRUHNWPRHYLyqHgv8GXgsXkY2Y5ExxvSeQGYsUtUKVW30T/8bmBlM8YwxxiQqkBmLRGRkzNOLcRNhGGOM6UOdXuWiqhERaZ2xKAw80jpjEbBEVV8EbvazF0WAPcB1vVhmY4wxcdiMRcYYM4DYjEXGGHMYsIBujDEpwgK6McakCAvoxhiTIiygG2NMirCAbowxKcICujHGpAgL6MYYkyIsoBtjTIoIZMaimHTzRERFJO5dTMYYY3pPpwE9ZsaiucAU4EoRmRInXT5wM7A46EIaY4zpXJAzFv0r8COgIcDyGWOMSVAgMxaJyPHAGFX9U0cZ2YxFxhjTe3o8Y5GIhICfAv/YWUY2Y5ExxvSeIGYsygemAQtEZDPwGeBF6xg1xpi+1eMZi1R1r6oOVdXxqjoeWARcrKo22LkxxvShTgO6qkaA1hmL1gDPtM5Y5GcpMsYY0w90OgUdgKrOB+a3ee377aSd0/NiGWOM6Sq7U9QYY1KEBXRjjEkRFtCNMSZFWEA3xpgUYQHdGGNShAV0Y4xJERbQjTEmRVhAN8aYFGEB3RhjUkQgMxaJyDdEZIWILBORv8WbAMMYY0zvCmrGoidUdbqqHoeb5OI/Ai+pMcaYDgUyY5Gq7ot5mkvMeOnGGGP6RiKDc8WbsWh220Qi8k3gO0AG8Nl4GYnIjcCNAGPHju1qWY0xxnSgxzMW7X9B9b9UdRLwPeCf42VkMxYZY0zvSaSG3tmMRW09BTzQncI0NzdTWlpKQ8PAmGc6KyuLkpIS0tPTk10UY4xJKKDvn7EI2Iabseiq2AQicqSqfuKfXgB8QjeUlpaSn5/P+PHjEYl3YtB/qCoVFRWUlpYyYcKEZBfHGGM6D+iqGhGR1hmLwsAjrTMWAUtU9UXgJhH5HNAMVALXdqcwDQ0NAyKYA4gIQ4YMoby8PNlFMcYYIKAZi1T1lqAKNBCCeauBVFZjTOqzO0WNMSZFWEA3xpgUYQHdGGNSREoE9NraWi644AJmzJjBtGnTePrpp1m6dClnnnkmM2fO5Nxzz2XHjh1EIhFOPPFEFixYAMCdd97JXXfdldzCG2NMQBLqFO3vXnnlFUaNGsVLL70EwN69e5k7dy4vvPACxcXFPP3009x111088sgjPProo8ybN4/777+fV155hcWLFye59MYYE4yUCOjTp0/ntttu43vf+x4XXnghRUVFrFy5knPOOQeAaDTKyJEjAZg6dSrXXHMNF110EQsXLiQjIyOZRTfGmMCkRECfPHkyS5cuZf78+dx5552cc845TJ06lYULF8ZNv2LFCgoLCykrK+vjkhpjTO9JiTb07du3k5OTw9VXX81tt93G4sWLKS8v3x/Qm5ubWbVqFQB/+MMfqKio4O233+bmm2+mqqoqmUU3xpjApEQNfcWKFdx+++2EQiHS09N54IEHSEtL4+abb2bv3r1EIhFuvfVWhg8fzh133MEbb7zBmDFjuOmmm7jlllt47LHHkv0VjDGmx0S186HLReQ84Oe4W/8fVtX72qz/DvA1IAKUA9er6paO8pw1a5YuWbLkoNfWrFnDMccc06UvkGwDsczGmIFLRJaq6qx464KasehDYJaqHgs8i5u1yBhjTB8KasaiN1W1zj9dhBti1xhjTB9KJKDHm7FodAfpbwBe7kmhjDHGdF0inaIJzVgEICJXA7OAM9tZb1PQGWNML0mkhp7QjEV+PPS7gItVtTFeRjYFnTHG9J5EAvr+GYtEJAM3Y9GLsQlE5Hjgl7hgviv4YhpjjOlMUDMW/RjIA37vJ334VFUv7sVy95pwOMz06dP3P3/++ecZP3588gpkjDEJCmrGos8FXK6kyc7OZtmyZckuhjHGdFm/vVP0nj+uYvX2fYHmOWXUIH5w0dRA8zTGmP6i3wb0ZKmvr+e4444DYMKECTz33HNJLpExxiSm3wb0ZNWkrcnFGDNQpcRoi8YYYyygG2NMyrCAbowxKcICehs1NTXJLoIxxnSLBXRjjEkRFtCNMSZFWEA3xpgUkVBAF5HzRGStiKwXkTvirD9DRD4QkYiIzAu+mMYYYzoT1BR0nwLXAU8EXUBjjDGJSeRO0f1T0AGISOsUdKtbE6jqZr+upRfKaIwxJgG9MQVdu0TkRhFZIiJLysvLu5NFrxMRrrnmmv3PI5EIxcXFXHjhhUkslTHGdC6RgJ7wFHSdGQgzFuXm5rJy5Urq6+sBeP311xk9ulvHL2OM6VOJNLkkNAVd4F6+A3auCDbPEdNh7n2dJps7dy4vvfQS8+bN48knn+TKK6/kr3/9a7BlMcaYgAUyBV2queKKK3jqqadoaGhg+fLlzJ49O9lFMsaYTgUyBZ2InAg8BxQBF4nIParas/FvE6hJ95Zjjz2WzZs38+STT3L++ecnrRzGGNMVQU1B9z6uKSZlXHzxxdx2220sWLCAioqKZBfHGGM61W8nuEi266+/noKCAqZPn86CBQuSXRxjjOmU3frfjpKSEm655ZZkF8MYYxJmNfQ24g2fO2fOHObMmdP3hTHGmC6wGroxxqQIC+jGGJMi+l1AV+3WTahJMZDKaoxJff0qoGdlZVFRUTEgAqWqUlFRQVZWVrKLYowxQD/rFC0pKaG0tJT+OnBXW1lZWZSUpNTl98aYAaxfBfT09HQmTJiQ7GIYY8yAFNSMRZki8rRfv1hExgddUGOMMR0LasaiG4BKVT0C+Cnwb0EX1BhjTMcSqaHvn7FIVZuA1hmLYl0CPOaXnwXOFpF446gbY4zpJYm0ocebsajteLL70/jRGfcCQ4DdsYlE5EbgRv+0RkTWdqfQwNC2eQesN/O3svd93r2dv5U9OfkP1Lx7mv+49lYkEtATmbEooVmNVPUh4KEEPrPjAoksUdVZPc0nGflb2fs+797O38qenPwHat69mX8iTS6JzFi0P42IpAEFwJ4gCmiMMSYxQc1Y9CJwrV+eB/xFB8LdQcYYk0ICmbEI+BXwGxFZj6uZX9GbhSaAZpsk5m9l7/u8ezt/K3ty8h+oefda/mIVaWOMSQ39aiwXY4wx3WcB3RhjUoWq9osH8AiwC1gZ89pxwCJgGbAEOMm/XgD8EfgIWAV8NeY91wKf+Me13ci7CHgOWA68B0yLec95wFpgPXBHJ2WfASwEVviyDopZd6fPYy1wbkf5dyVv3LX/bwI1wC/abN+ZPv164H4ONLd1Jf9zgKX+9aXAZzvKv4t5n+T3xTK/X78Y1HaJWT/Wb5vbgtynwHigPqb8Dwa1Xfy6Y/26VX59VoD79Msx5V4GtADHBbRP03E3HK4A1gB3BvhbzwB+7V//CJiTwG99DO7/Y43flrf41wcDr+NixutAkX9d/PvX4+LBCe3FmW7kfbT/Xo3E/B47+k0mFEeTHchjvsQZwAltduZrwFy/fD6wwC//E/BvfrkY1xGb4TfeRv+3yC8XdTHvHwM/iNnob/jlMLABmOg/6yNgSgdlfx840y9fD/yrX57i35sJTPB5htvLv4t55wKnAd/g0ID+HnCy/5G+HPPdu5L/8cAovzwN2NZR/l3MOwdI88sjcf/YaUFsl5j1/wP8Hv8PFOA+HR+brrPt3sW803DBZIZ/PgQIB7VP25R1OrAxwLJfBTwVs383+20VxG/9m8Cv/fIwXAUj1Ml2GYkPykA+sM5/7o84cFC5gwOx5Xz/fgE+Ayz2r8eLM0d3Me9hwInAvRxcwWj3N5nIo980uajq2xx67boCg/xyAQeuf1cg3w8vkOffFwHOBV5X1T2qWok7Ip7XxbynAG/4Mn0MjBeR4XQwBEI7+R8FvO2XXwcu9cuX4H7kjaq6CXcUPqm9/LuSt6rWqurfgIbYxCIyElezWajuV/M48IWull1VP1TV1u20CsjyA7PFzb+LedepasS/nsWBG9N6vF38NvgC7h9vVUz6oPZpXEFsF+DzwHJV/ciXq0JVo0Ht0zauBJ4MsOwK5Pp7U7KBJmAfwezT2P/TXUAVMKuT7bJDVT/wy9W42vRoDh665LHW9P71x9VZBBT6/OPFmeO7kreq7lLV94HmNt83kaFW2tVvAno7bgV+LCJbgZ/gmioAfgEcgwvCK3CnNy3EH6ZgdBfz/gj4EoCInIS7zbaki3kDrAQu9suXceDmrPby6Ur+7eXdntE+v56WPdalwIeq2tjF/NvNW0Rmi0hrs8I3fIDv8XYRkVzge8A9bdIHtU8BJojIhyLyloicHpN/T7fLZEBF5FUR+UBEvtuNvDsre6vL8QE9oLI/C9QCO4BPgZ+o6h6C+a1/BFwiImkiMgHXzDIm0XL7EWGPBxYDw1V1B7igj6s900E5Oyx/gnm3p6u/yYP094D+v4Bvq+oY4Nu4693BHSGXAaNwbeG/EJFBJDgEQSd53wcUicgy4FvAh7jaf1fyBnd6+E0RWYo7BWvyr7eXT1fyby/v9gRVdpeZyFTciJp/3438281bVRer6lTcqeidIpIVUN73AD9V1Zo26YPaLjuAsap6PPAd4Ilu/B7byzsN14z2Zf/3iyJydoBlB9zBFKhT1ZWtLwVQ9pOAKO7/dALwjyIyMaC8H8EFuyXAz4B3SfD/VETycM1vt6rqvnY+lw7yavczupB3Vz8zIf1qgos4rgVu8cu/Bx72y18F7vOnVOtFZBOuDasUmBPz/hJgQVfy9jvhqwC+SWeTf+TQ+RAI+/nmms/7fCYDF/hVHQ2lkFD+HeTdnlKfX0/LjoiU4DqNv6KqG7qafyJlV9U1IlKLa6dPZOiJzvKeDcwTkR8BhUCLiDTg2l17vE/9WUqjX14qIhtwNesgtksp8Jaq7vbr5uPamX+baN6d5N/qCg7Uzls/t6dlvwp4RVWbgV0i8g4wC1cD7dE+9Wdv325NJyLv4joeKzsqt4ik4wLu71T1D/7lMhEZqao7fJPKrphtEK+cceNMF/NuT8K/97gSbWzviwdtOpdw7VBz/PLZwFK//ABwt18eDmzDjV42GBd8i/xjEzC4i3kXAhl++eu4NjRwB7+NuJpGa2fF1A7KPsz/DeHa8a73z6dycKfoRlxHSLv5J5p3zPrrOLRT9H1cx05rR9H53Sh7oS/XpXH2Xdz8u5D3BA50io7D/YiHBrld/Lq7OdApGtQ+LeZAR+VE3O9xcEDbpQj4AN9pDPwZuCCofRrzWikwMeB9+j3clSiC67Bfjbtip8f71G+PXL98DvB2AuUWn8fP2nzPH3Nwx+WP/PIFHNwp+p5/PW6c6Ure8X6PifwmO42hXQ26vfXA1Q524DoJSnGTZpyGq0V9hGuPmunTjsJdpbIC18Z2dUw+1+M6GtfjL2fsYt4n4470HwN/wF9m5Nedj+u93gDc1UnZb/Fp1+GacSQm/V0+j7X4Hvj28u9G3ptxHUs1Pn3rVRuz/LbagOuDkK7mD/wzrk009jK3Ye3l38W8r8F1WC7DBbAvBLldOvgH6vE+xfUnrML9lj4ALorJp0fbxedxtc9/JTEBIYh96tPPARbF2VY93ad5uLPfVbhgfntQ+xQX+NfiKmZ/BsYlsF1OwzVfLOfA7/d83JVDb+D+79/gwMFYcJP7bMDFmlntxZlu5D3Cf8d9uA7dUg5ckhn3N5nIw279N8aYFNHfO0WNMcYkyAK6McakCAvoxhiTIiygG2NMirCAbowxKcICujHGpAgL6Mb0gIiEk10GY1pZQDeHDRH5VxG5Jeb5vSJys4jcLiLvi8hyEbknZv3zIrJURFaJyI0xr9eIyL+IyGLcjWjG9AsW0M3h5Fe4MXwQkRBu/JIy4EjcQFLHATNF5Ayf/npVnYm78/BmERniX8/F3Z4+W91wxcb0C/19cC5jAqOqm0WkQkSOx40B9CFuZMfP+2Vwt6sfiRuD+2YR+aJ/fYx/vQI3guD/9GXZjUmEBXRzuHkYN3jZCNwQrGcDP1TVX8YmEpE5wOeAk1W1TkQW4CbeAGhQ1WhfFdiYRFmTizncPIebs/FE4FX/uN6PY42IjBaRYbhZrCp9MD8aN9qeMf2a1dDNYUVVm0TkTaDK17JfE5FjgIVu+HtqcKMbvgJ8Q0SW40b1W5SsMhuTKBtt0RxWfGfoB8BlqvpJsstjTJCsycUcNkRkCm786jcsmJtUZDV0Y4xJEVZDN8aYFGEB3RhjUoQFdGOMSREW0I0xJkVYQDfGmBTx/wHpmWTuHhXOJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 评估命名多样性的增长\n",
    "\n",
    "# 一种解释是父母原意给小孩起常见的名字越来越少\n",
    "# 这个假设可以从数据中得到验证\n",
    "# 一个办法是计算最流行的 1000 个名字所占的比例\n",
    "# 按 year 和 sex 进行聚合并绘图\n",
    "\n",
    "table = top1000.pivot_table('prop', index='year', columns='sex', aggfunc=sum)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "table.plot(title=\"Sum of table1000.prop by year and sex\", yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births  year      prop\n",
       "260877    Jacob   M   21875  2010  0.011523\n",
       "260878    Ethan   M   17866  2010  0.009411\n",
       "260879  Michael   M   17133  2010  0.009025\n",
       "260880   Jayden   M   17030  2010  0.008971\n",
       "260881  William   M   16870  2010  0.008887\n",
       "...         ...  ..     ...   ...       ...\n",
       "261872   Camilo   M     194  2010  0.000102\n",
       "261873   Destin   M     194  2010  0.000102\n",
       "261874   Jaquan   M     194  2010  0.000102\n",
       "261875   Jaydan   M     194  2010  0.000102\n",
       "261876   Maxton   M     193  2010  0.000102\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从上图可知，名字的多样性确实出现了增长（前 1000 项的比例降低了）\n",
    "\n",
    "# 另一个方法是计算占出生人数前 50% 的不同名字的数量\n",
    "# 这个数字不好计算，只考虑 2010 年男孩的名字\n",
    "\n",
    "df = boys[boys.year == 2010]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在对 prop 降序排列之后\n",
    "# 想知道前面多少个名字的人数加起来才够 50% \n",
    "# 虽然编写一个 for 循环能够达到目的，但是 numpy 有一种更聪明的矢量方式\n",
    "# 先计算 prop 的累计和 cumsum ，然后通过 searchsorted 方法找出 0.5 应该被插入在那个位置才能保证不破坏顺序\n",
    "\n",
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "\n",
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于数组索引是从 0 开始，因此要给次结果加 1 ，最终结果为 117\n",
    "# 拿 1900 年的数据来作了比较，这个数字小的多\n",
    "\n",
    "df = boys[boys.year == 1900]\n",
    "\n",
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "\n",
    "in1900.values.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  38  14\n",
       "1881  38  14\n",
       "1882  38  15\n",
       "1883  39  15\n",
       "1884  39  16"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 现在可以对所有 year/sex 组合执行这个计算\n",
    "# 按这两个字段进行 groupby 处理，然后用一个函数计算分组的这个值\n",
    "\n",
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(by='prop', ascending=False)\n",
    "    return group.prop.cumsum().searchsorted(q) + 1\n",
    "\n",
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
    "\n",
    "diversity = diversity.unstack('sex')\n",
    "\n",
    "# 现在，diversity 这个 DataFrame 拥有两个时间序列（每个性别一个，按年度索引）\n",
    "# 通过 IPython 可以查看其内容，也可以绘制图表\n",
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bad0b08>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title=\"Number of popular names in top 50%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从图中看出，女孩名字的多样性总是比男孩的高，而且还在变得越来越高\n",
    "# 这个趋势发生的原因，可以具体分析一下是什么在驱动这个多样性（比如拼写形式的变化）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# “ 最后一个字母 ” 的变革\n",
    "\n",
    "# 2007年， 一名婴儿姓名研究人员 Laura Wattenberg 在她自己的网站上指出 （http://www.babynamewizard.com）：\n",
    "#      近百年来， 男孩名字在最后一个字母上的分布发生了显著的变化。\n",
    "\n",
    "# 为了了解具体的情况，首先将全部出生数据在年度、 性别以及末字母上进行了聚合\n",
    "# extract last letter from name column\n",
    "get_last_letter = lambda x: x[-1]\n",
    "last_letters = names.name.map(get_last_letter)\n",
    "last_letters.name = 'last_letter'\n",
    "\n",
    "table = names.pivot_table('births', index=last_letters, columns=['sex', 'year'], aggfunc=sum)\n",
    "\n",
    "# 然后，选出具有一定代表性的三年， 并输出前面几行\n",
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 接下来需要按总出生数对该表进行规范化处理\n",
    "#   以便于计算出各性别末尾字母占总出生人数的比例\n",
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>0.009488</td>\n",
       "      <td>0.001404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.051529</td>\n",
       "      <td>0.036224</td>\n",
       "      <td>0.075852</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>0.037907</td>\n",
       "      <td>0.051670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.039965</td>\n",
       "      <td>0.031734</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.022628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.049384</td>\n",
       "      <td>0.018541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>0.043189</td>\n",
       "      <td>0.033867</td>\n",
       "      <td>0.026356</td>\n",
       "      <td>0.065016</td>\n",
       "      <td>0.104904</td>\n",
       "      <td>0.070367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.008613</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.058044</td>\n",
       "      <td>0.033827</td>\n",
       "      <td>0.024657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.079240</td>\n",
       "      <td>0.130687</td>\n",
       "      <td>0.140210</td>\n",
       "      <td>0.143415</td>\n",
       "      <td>0.152522</td>\n",
       "      <td>0.362771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.017065</td>\n",
       "      <td>0.012829</td>\n",
       "      <td>0.042681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.005675</td>\n",
       "      <td>0.001269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>0.064481</td>\n",
       "      <td>0.031034</td>\n",
       "      <td>0.087477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.039042</td>\n",
       "      <td>0.012764</td>\n",
       "      <td>0.013332</td>\n",
       "      <td>0.130815</td>\n",
       "      <td>0.102730</td>\n",
       "      <td>0.065145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.027438</td>\n",
       "      <td>0.015201</td>\n",
       "      <td>0.007830</td>\n",
       "      <td>0.072879</td>\n",
       "      <td>0.065655</td>\n",
       "      <td>0.022861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>0.000684</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.001221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.016148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.110972</td>\n",
       "      <td>0.152569</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.160987</td>\n",
       "      <td>0.058168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "f                 NaN  0.000010  0.000055  0.000783  0.004325  0.001188\n",
       "g            0.000144  0.000157  0.000374  0.002250  0.009488  0.001404\n",
       "h            0.051529  0.036224  0.075852  0.045562  0.037907  0.051670\n",
       "i            0.001526  0.039965  0.031734  0.000844  0.000603  0.022628\n",
       "j                 NaN       NaN  0.000090       NaN       NaN  0.000769\n",
       "k            0.000121  0.000156  0.000356  0.036581  0.049384  0.018541\n",
       "l            0.043189  0.033867  0.026356  0.065016  0.104904  0.070367\n",
       "m            0.001201  0.008613  0.002588  0.058044  0.033827  0.024657\n",
       "n            0.079240  0.130687  0.140210  0.143415  0.152522  0.362771\n",
       "o            0.001660  0.002439  0.001243  0.017065  0.012829  0.042681\n",
       "p            0.000018  0.000023  0.000020  0.003172  0.005675  0.001269\n",
       "q                 NaN       NaN  0.000030       NaN       NaN  0.000180\n",
       "r            0.013390  0.006764  0.018025  0.064481  0.031034  0.087477\n",
       "s            0.039042  0.012764  0.013332  0.130815  0.102730  0.065145\n",
       "t            0.027438  0.015201  0.007830  0.072879  0.065655  0.022861\n",
       "u            0.000684  0.000574  0.000417  0.000124  0.000057  0.001221\n",
       "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
       "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
       "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
       "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
       "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = subtable / subtable.sum()\n",
    "\n",
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bba0cc8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 有了这个字母比例数据之后\n",
    "#  就可以生成一张各个年度各性别的条形图\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "\n",
    "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
    "\n",
    "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female', legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以看出，从 20 世纪 60 年代 开始，\n",
    "#  以字母 'n' 结尾的男孩名字出现了显著的增长\n",
    "\n",
    "# 回到之前创建的那个完整表，按年度和性别对其进行规范化处理，并在男孩名字中选取几个名字\n",
    "# 最后进行转置以便于各个列做成一个时间序列\n",
    "\n",
    "letter_prop = table / table.sum()\n",
    "\n",
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T\n",
    "\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9e6988>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 有了这个时间序列的 DataFrame 之后，\n",
    "#  就可以通过其 plot 方法绘制出一张趋势图\n",
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 变成女孩名字的男孩名字（以及相反的情况）\n",
    "\n",
    "# 另一个有趣的趋势是，早年流行于男孩的名字近年来 “ 变性了 ”\n",
    "# 例如 Lesley 或者 Leslie\n",
    "# 回到 top1000 数据集，找出其中以 'lesl' 开头的一组名字\n",
    "\n",
    "all_names = pd.Series(top1000.name.unique())\n",
    "\n",
    "lesley_like = all_names[all_names.str.lower().str.contains('lesl')]\n",
    "\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 然后利用这个结果过滤其他的名字\n",
    "#   并按名字分组计算出生数已查看相对频率\n",
    "\n",
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex     F   M\n",
       "year         \n",
       "2006  1.0 NaN\n",
       "2007  1.0 NaN\n",
       "2008  1.0 NaN\n",
       "2009  1.0 NaN\n",
       "2010  1.0 NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 接下来，按性别和年度进行聚合，并按年度进行规范化处理\n",
    "table = filtered.pivot_table('births', index='year', columns='sex', aggfunc='sum')\n",
    "\n",
    "table = table.div(table.sum(1), axis=0)\n",
    "\n",
    "table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cc90f48>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 最后，可以轻松绘制一张分性别的年度曲线图\n",
    "table.plot(style={'M': 'k-', 'F': 'k--'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
